Abstract

The ability to form relational categories for objects that share
few features in common is a hallmark of human cognition. However until recently,
neuroimaging research largely focused solely on how people acquire categories
defined by features. In the current electroencephalography (EEG) study, we
examine how relational and feature-based category learning compare in
well-matched learning tasks. Building on a previous functional magnetic resonance
imaging study by our laboratory, we capitalise on the rich temporal information
offered by EEG. Focusing on the neural dynamics of how people learn category
memberships over individual trials in an experimental task, we investigate how
these single trial dynamics modulate computational estimates from decision-making
modelling frameworks. Specifically, by sorting participants’ individual
trials by their position in the experimental sequence we observe striking
relationships between EEG dynamics (e.g., frontal theta oscillations and P300
component) and feature-based and relational categorisation behaviour.